Big Five Personality Traits Inference from Five Facial Shapes Using CNN

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

Limited accuracy and speed in facial recognition (FR) and in the identification of facial emotions (IFE) have been shown in autism spectrum disorders (ASD). This study aimed at evaluating the predictive value of atypicalities in FR and IFE for future symptom severity in children with ASD. Therefore we performed a seven-year follow-up study in 87 children with ASD. FR and IFE were assessed in childhood (T1: age 6-12) using the Amsterdam Neuropsychological Tasks (ANT). Symptom severity was assessed using the Autism Diagnostic Observation Schedule (ADOS) in childhood and again seven years later during adolescence (T2: age 12-19). Multiple regression analyses were performed to investigate whether FR and IFE in childhood predicted ASD symptom severity in adolescence, while controlling for ASD symptom severity in childhood. We found that more accurate FR significantly predicted lower adolescent ASD symptom severity scores (ΔR(2) = .09), even when controlling for childhood ASD symptom severity. IFE was not a significant predictor of ASD symptom severity in adolescence. From these results it can be concluded, that in children with ASD the accuracy of FR in childhood is a relevant predictor of ASD symptom severity in adolescence. Test results on FR in children with ASD may have prognostic value regarding later symptom severity.

Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.

This paper reviews state-of-the-art developments in facial emotion identification with the help of Deep learning technology. This study has reviewed the recent research developments in facial emotion recognition, along with the performance of leading deep learning architectures and algorithms. Further, this study discusses about the contribution, model performance, and limitations of various architectures like Convolutional Neural Network (CNNs) and Recurrent Neural Networks (RNNs). Moreover, this study has examined the effectiveness of various cutting-edge deep learning algorithms for detecting facial expressions. As a result, it has been found that the most successful architectures are Hybrid CNN-RNNs, which combine convolutional layers with recurrent ones. This is due to their ability to learn hierarchical representations of data and exploit temporal dependencies in inputs. There are also architectures that use meta-parameters such as attention vectors or word embeddings; however, they do not provide significant improvements over RNNs alone. Also, this study covered the application of deep learning to facial emotion recognition, as well as its potential limits. Next, this study discusses about some research works that can be done in this field. This research study reviews the literature on face recognition. It also explains the relationship between facial emotion and facial feature extraction, which is essential for emotion recognition.

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Facial expressions recognition and synthesis are important research fields to study how human beings reflect to environments in affective computing. With the rapid development of mathematical theory on multivariate statistics and multi-media technology especially image processing, facial expressions recognition researchers have achieved many useful results. Recently studies show that approaches to facial modeling and expressions recognition and synthesis analysis could be adapted to control security or even real-time health monitoring in the real world. Similarity, how to achieve facial expressions recognition and synthesis for independent-free mechanism is a central design in general. Based on the cognitive analysis of independent user's affective facial recognition researches we proposed an emotions composition model to dope out what are the user's new affective facial expressions. At this point, principal component, cluster and discriminate analysis were applied to show and verify independent user's affective expressions can be composited for synthesis by basic facial expressions such as happy, neutral and unhappy. Experiments were conducted to prove our models to be very significant.

Recognition of Facial expression in technology plays a major role in many sectors. It has many advantages because of which it is very important. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural networks (CNN). The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them. OpenCV is used for automatic detection of faces and drawing bounding boxes around them. Face detection using the Hear cascades is a machine learning based algorithm where a cascade function will be trained with a set of input data. OpenCV contains many pre-trained classifiers for face, eyes, smile etc. The deep learning is a subset of machine learning. Deep learning is used by Google to translate the information form one language to another using deep learning approach. The network should be trained with relatively more data in deep learning.

This study explored the transformative effects of AI-driven facial recognition technology on identity verification systems. It delves into the recent progress in computer vision and deep learning algorithms, which have greatly improved the accuracy and speed of facial recognition. This study highlights the potential uses of this technology in various fields such as security, finance, and personalized services. It also examines the ethical issues and privacy concerns arising from the widespread adoption of facial recognition. Furthermore, this study addresses the challenges of creating robust systems that can manage diverse populations and environmental conditions. Finally, it outlines future research and development directions for facial recognition technology, stressing the importance of responsible innovation and regulatory frameworks. Keywords — Face Recognition, Artificial Intelligence, Computer Vision, Identity Verification, Biometrics, Privacy, Security, Deep Learning, Feature Extraction, Multimodal Biometrics.

Face detection is a key problem in intelligent visual human computer interaction research. It is essential to many applications, for example, human face synthesis, face recognition, face tracking, pose estimation, facial expression recognition and object oriented image coding. This paper presents a new face detection algorithm based on Hough Transform and facial features. First, uses geometry match, calculates the mouth location. Then, according to the triangle relation between eyes and mouth, make a verification of face rotation. Finally, realizes the human face detection accurately. Experimental results show that this algorithm can be used in different environment, and it can also improve the face recognition obviously.KeywordsHough transformmouth locationgeometry characteristicsevaluation function

In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.

Unfolding Explainable AI for Brain Tumor Segmentation

Fairness has become an important agenda in computer vision and artificial intelligence. Recent studies have shown that many computer vision models and datasets exhibit demographic biases and proposed mitigation strategies. These works attempt to address accuracy disparity, spurious correlations, or unbalanced representations in datasets in tasks such as face recognition, verification and expression and attribute classification. These tasks, however, all require face detection as the first preprocessing step, and surprisingly, there has been little effort in identifying or mitigating biases in face detection. Biased face detectors themselves pose a threat against fair and ethical AI systems, and their biases may be further passed on to subsequent downstream tasks such as face recognition in a computer vision pipeline. This paper therefore investigates the problem of biases in face detection, focusing on accuracy disparity of detectors between demographic groups including gender, age group, and skin tone. We collect perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE, report skewed demographic distributions, and compare detection performance between groups. In order to mitigate the biases, we apply three mitigation methods that have been introduced in the recent literature and also propose two novel methods. Experimental results show that these methods are effective in reducing demographic biases. We also discuss how the effectiveness varies by demographic attributes, detection easiness, and multiple detectors, which will shed light on this new topic of addressing face detection bias.

In the rapidly evolving field of artificial intelligence, machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools for solving complex problems in various domains, including cyber security. However, as these algorithms become increasingly prevalent, they also face new security challenges. One of the most significant of these challenges is the threat of zero-day attacks, which exploit unknown and unpredictable vulnerabilities in the algorithms or the data they process. This paper provides a comprehensive overview of zero-day attacks on ML/DL algorithms, exploring their types, causes, effects, and potential countermeasures. The paper begins by introducing the concept and definition of zero-day attacks, providing a clear understanding of this emerging threat. It then reviews the existing research on zero-day attacks on ML/DL algorithms, focusing on three main categories: data poisoning attacks, adversarial input attacks, and model stealing attacks. Each of these attack types poses unique challenges and requires specific countermeasures. The paper also discusses the potential impacts and risks of these attacks on various application domains. For instance, in facial expression recognition, an adversarial input attack could lead to misclassification of emotions, with serious implications for user experience and system integrity. In object classification, a data poisoning attack could cause the algorithm to misidentify critical objects, potentially endangering human lives in applications like autonomous driving. In satellite intersection recognition, a model stealing attack could compromise national security by revealing sensitive information. Finally, the paper presents some possible protection methods against zero-day attacks on ML/DL algorithms. These include anomaly detection techniques to identify unusual patterns in the data or the algorithm’s behaviour, model verification and validation methods to ensure the algorithm’s correctness and robustness, federated learning approaches to protect the privacy of the training data, and differential privacy techniques to add noise to the data or the algorithm’s outputs to prevent information leakage. The paper concludes by highlighting some open issues and future directions for research in this area, emphasizing the need for ongoing efforts to secure ML/DL algorithms against zero-day attacks.

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques